What the Data for This Category Looks Like
Livestock and poultry farming research report data originates from publicly available monitoring data published by the Ministry of Agriculture and Rural Affairs’ Animal Husbandry and Veterinary Bureau, monthly monitoring reports from industry associations, quarterly analysis documents from professional breeding consulting institutions, and regular operating announcements released by listed breeding enterprises. Updates follow a monthly routine schedule. Core supply and demand data releases each month. Quarterly in-depth research reports go live within 15 working days after the end of each quarter. Temporary supplementary documents are generated during animal disease outbreaks or raw material price fluctuations. Typical document structure includes four sections: core breeding indicators, regional supply and demand distribution, raw material cost analysis, and future market outlook. Core fields include fertile sow inventory, white feather chicken slaughter volume, average live pig slaughter price, and others. Units are uniformly ten thousand heads, kilograms, and yuan per kilogram.
Constraints Imposed on Knowledge Base Retrieval and Recall
The multi-source, high-frequency update, and multi-field characteristics of livestock and poultry farming research reports create multiple constraints for the retrieval and recall process. Different institutions release similar indicators with different statistical calibers. Field alignment must be completed during the preprocessing stage to avoid data conflicts in recalled content. Routine monthly updated data and temporary emergency documents coexist. The knowledge base must support incremental synchronization and fast batch uploads to maintain data timeliness. Core indicator fields are numerous and have strict unit requirements. Retrieval must prioritize matching specified fields to avoid recalling irrelevant content. Long documents must be segmented while retaining the connection between indicators and their context. This prevents loss of critical logic after splitting.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall count | Top 6-8 entries | Core indicators in livestock and poultry farming research reports are dense. Too many recalled entries cause redundant context. Too few fail to cover complete analysis logic |
similarity threshold | 0.72-0.85 | Industry indicator terminology is highly specialized. A threshold that is too low introduces irrelevant documents. A threshold that is too high may miss valid analysis in the same field |
chunk length | 800-1200 characters | Core indicator paragraphs in single research reports are approximately 1000 characters. Segmentation retains the connection between indicators and analysis, avoiding context breaks after splitting |
field matching weight | Set core indicator fields to 1.5, general analysis fields to 1.0 | Core indicators of livestock and poultry farming research reports are the focus of retrieval. Raising their matching priority improves recall accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Parsing long quarterly research reports takes significant time. This avoids upload failures caused by timeouts |
incremental sync switch | Enabled | Routine monthly updated data requires fast synchronization. Incremental sync reduces resource consumption from full updates |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Analyze specific issues on a case-by-case basis. Test against relevant samples before finalizing settings.
Three Common Misconfigurations
- Issue: After customizing the
chunk lengthsetting, retrieved content deviates significantly from the core logic of the original research report. Cause: Segmentation fails to retain the connection between core indicators and subsequent analysis. Critical data breaks away from context after splitting, leading to broken logic in recalled content. - Issue: Setting the
similarity thresholdto 1 still results in a large number of low-relevance reference entries in response results. Cause: Field alignment has not been completed for documents from different sources, or matching logic has not been adjusted for industry terminology. This causes the system to misjudge content with different calibers as highly relevant. - Issue: Retrieval response time reaches approximately 15 seconds, and some requests trigger timeout errors. Cause: Parsing result caching is not enabled, and the
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted. Long quarterly research reports require re-parsing for each retrieval, leading to excessive latency.
How to Confirm Proper Configuration
- Upload a single core quarterly research report. Review the parsed segmented content to confirm that core indicators and their analysis context are not split apart.
- Enter specified core indicator keywords. Confirm that documents containing the corresponding fields appear first in retrieval results, verifying that the field matching weight configuration takes effect.
- Send multiple retrieval requests. Compare response times to confirm that long document parsing results have been cached, and latency stabilizes within a reasonable range.
- Adjust the
similarity threshold. Observe changes in the number of recalled results to confirm that configuration parameters affect retrieval logic as expected.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.